 We selected the artificial neural network because it is considered as a universal function approximators [1]}, [2]}, but also because it does not need any prior knowledge about the structure of the equation to best fit the training data, similar to the symbolic regression algorithm. Contrary to symbolic regression, the product of a neural network approach is not a function but the trained neural network itself. We will use a feed-forward deep neural network, with eight hidden layers. The first hidden layer consists of 64 neurons, while, the remaining hidden layers contain 32 neurons, and finally an output layer containing a single neuron [3]}. In each hidden layer we use the Relu activation function, and also we apply L2 regularization to avoid overfitting. The algorithm minimizes the mean square difference between the predicted and training data, using a gradient descent algorithm.  We use the open-source library TensorFlow [4]} to implement the artificial neural network framework. For training, we use 40,000 training points, which is a much higher volume compared to the other two algorithms, because deep neural networks require a large amount of data to be trained appropriately[5]}.
